Atmospheric Radiation Measurement Southern Great Plains Research Articles

Case study of a bore wind-ramp event from lidar measurements and HRRR simulations over ARM Southern Great Plains

The rapid change of wind speed and direction on 21 August 2017 is studied using Doppler lidar measurements at five sites of the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility in north-central Oklahoma. The Doppler lidar data were investigated along with meteorological variables such as temperature, humidity, and turbulence available from the large suite of instrumentation deployed at the SGP Central Facility (C1) during the Land-Atmosphere Feedback Experiment in August 2017. Lidar measurements at five sites, separated by 55–70 km, allowed us to document the development and evolution of the wind flow over the SGP area, examine synoptic conditions to understand the mechanism that leads to the ramp event, and estimate the ability of the High-Resolution Rapid Refresh model to reproduce this event. The flow feature in question is an atmospheric bore, a small-scale phenomenon that is challenging to represent in models, that was generated by a thunderstorm outflow northwest of the ARM SGP area. The small-scale nature of bores, its impact on power generation, and the modeling challenges associated with representing bores are discussed in this paper. The results also provide information about model errors between sites of different surface and vegetation types.

ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing

Abstract There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at the ARM SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear-air images and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k-means clustering identifies 10 clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud-base heights.

Evaluation of the Near-Surface Variables in the HRRR Weather Model Using Observations from the ARM SGP Site

Abstract The performance of version 4 of the NOAA High-Resolution Rapid Refresh (HRRR) numerical weather prediction model for near-surface variables, including wind, humidity, temperature, surface latent and sensible fluxes, and longwave and shortwave radiative fluxes, is examined over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) region. The study evaluated the model’s bias and bias-corrected mean absolute error relative to the observations on different time scales. Forecasts of near-surface geophysical variables at five SGP sites (HRRR at 3-km scale) were found to agree well with observations, but some consistent observation–forecast differences also occurred. Sensible and latent heat fluxes are the most challenging variables to be reproduced. The diurnal cycle is the main temporal scale affecting observation–forecast differences of the near-surface variables, and almost all of the variables showed different biases throughout the diurnal cycle. Results show that the overestimation of downward shortwave and the underestimation of downward longwave radiative flux are the two major biases found in this study. The timing and magnitude of downward longwave flux, wind speed, and sensible and latent heat fluxes are also different with contributions from model representations, data assimilation limitations, and differences in scales between HRRR and SGP sites. The positive bias in downward shortwave and negative bias in longwave radiation suggests that the model is underestimating cloud fraction in the study domain. The study concludes by showing a brief comparison with version 3 of the HRRR and shows that version 4 has better performance in almost all near-surface variables. Significance Statement A correct representation of the near-surface variables is important for numerical weather prediction models. This study investigates the capability of the latest NOAA High-Resolution Rapid Refresh (HRRRv4) model in simulating the near-surface variables by comparing against the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) in situ observations. Among others, we find that the surface heat fluxes, such as sensible and latent heat fluxes, are the most difficult variables to be reproduced. This study also shows that the diurnal cycle has the dominant impact on the model’s performance, which means the majority of the outputted near-surface variables have the strong diurnal cycle in their bias errors.

Aerosol Direct Radiative Effects at the ARM SGP and TWP Sites: Clear Skies

AbstractThe clear‐sky aerosol direct radiative effect (DRE) was estimated at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) and Tropical Western Pacific (TWP) sites. The NASA Langley Fu‐Liou radiation model was used with observed inputs including aerosol vertical extinction profile from the Raman lidar; spectral aerosol optical depth (AOD), single‐scattering albedo and asymmetry factor from Aerosol Robotic Network; temperature and water vapor profiles from radiosondes; and surface shortwave (SW) spectral albedo from radiometers. A radiative closure experiment was conducted for clear‐sky conditions. The mean differences of modeled and observed surface downwelling SW total fluxes were 1 W m−2 at SGP and 2 W m−2 at TWP, which are within observational uncertainty. At SGP, the estimated annual mean clear‐sky aerosol DRE is −3.00 W m−2 at the top of atmosphere (TOA) and −6.85 W m−2 at the surface. The strongest aerosol DRE of −4.81 (−10.77) W m−2 at the TOA (surface) are in the summer when AODs are largest. The weakest aerosol DRE of −1.28 (−2.77) W m−2 at the TOA (surface) are in November–January when AODs and single‐scattering albedos are lowest. At TWP, the annual mean clear‐sky DRE is −2.82 W m−2 at the TOA and −10.34 W m−2 at the surface. The strongest aerosol DRE of −5.95 (−22.20) W m−2 at the TOA (surface) are in November (October) due to the biomass burning season’s peak. The weakest aerosol DRE of −0.96 (−4.16) W m−2 at the TOA (surface) are in March (April) when AODs are smallest.

Long-Term Retrievals of Cloud Type and Fair-Weather Shallow Cumulus Events at the ARM SGP Site

AbstractA long-term climatology of classified cloud types has been generated for 13 years (1997–2009) over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for seven cloud categories: low clouds, congestus, deep convection, altocumulus, altostratus, cirrostratus/anvil, and cirrus. The classification was based on the cloud macrophysical quantities of cloud top, cloud base, and physical thickness of cloud layers, as measured by active sensors such as the millimeter-wavelength cloud radar (MMCR) and micropulse lidar (MPL). Climate variability of cloud characteristics has been examined using the 13-yr cloud-type retrieval. Low clouds and cirrus showed distinct diurnal and seasonal cycles. Total cloud occurrence followed the variation of low clouds, with a diurnal peak in early afternoon and a seasonal maximum in late winter. Additionally, further work has been done to identify fair-weather shallow cumulus (FWSC) events for 9 years (2000–08). Periods containing FWSC, a subcategory of clouds classified as low clouds, were produced using cloud fraction information from a total-sky imager and ceilometer. The identified FWSC periods in our study show good agreement with manually identified FWSC, missing only 6 cases out of 70 possible events during the spring to summer seasons (May–August).

Regional Moisture Budget and Land‐Atmosphere Coupling Over the U.S. Southern Great Plains Inferred From the ARM Long‐Term Observations

AbstractTen‐year warm‐season (May–August) observations at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to examine the large‐scale atmospheric moisture budget and the strength of land‐atmosphere coupling. Atmospheric moisture budget components, computed from the ARM variational analysis data, are compared between different wet/dry scenarios and convection regimes. Consistent with previous studies, large‐scale moisture flux convergence dominates the SGP regional moisture budget on the daily time scale, but surface evaporation does play a more prominent role on late‐afternoon deep convection days. Moreover, the relationship between moisture flux convergence and precipitation increases significantly from dry year to wet years. The land‐atmosphere coupling strength is quantified for local convective events within the “Local L‐A Coupling Process Chain” (Santanello et al., 2018, https://doi.org/10.1175/BAMS‐D‐17‐0001.1). The impact of land surface on the evolution of planetary boundary layer is highly dependent on the vegetation leaf area index. A significantly larger surface sensible heat flux is found over the SGP forest region in midsummer, which is accompanied by a much higher planetary boundary layer development and a large increase in shallow cumulus cloud fraction. The significant relationship between afternoon precipitation and surface turbulent flux only exists in the northeast part of the ARM SGP site, which is mainly covered by grassland/pasture.

Differences in Eddy‐Correlation and Energy‐Balance Surface Turbulent Heat Flux Measurements and Their Impacts on the Large‐Scale Forcing Fields at the ARM SGP Site

AbstractDifferences in the surface turbulent heat fluxes measured by the eddy correlation flux measurement system (ECOR) and the energy balance Bowen ratio system (EBBR) are examined using 12 years of continuous measurements collected at multiple stations in the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) surface observational network. The flux measurements are found strongly impacted by their upwind surface types, which vary with wind direction. For collocated ECOR and EBBR at the central facility, the fluxes measured by ECOR and EBBR agree much better when their upwind fetches are over the same surface type. Among all stations at SGP, ECOR measures more over winter wheat fields while EBBR measures mostly over grassland. The different seasonality of growth cycles between winter wheat and grass causes systematic differences in measured fluxes between ECOR and EBBR. These differences impact the derived large‐scale forcing as illustrated in a constrained variational analysis, in which the state variables have to be adjusted according to different fluxes to keep the column‐integrated energy and moisture budgets in balance. This impact prevails in summertime on nonprecipitating days. A single‐column model test shows that model‐simulated boundary layer development is impacted by using the large‐scale forcing data of different surface turbulent fluxes. It is recommended to include both ECOR and EBBR measurements to better represent the domain‐mean turbulent fluxes and atmospheric budgets of energy and water vapor at the SGP.

Differences in Ice Cloud Optical Depth From CALIPSO and Ground‐Based Raman Lidar at the ARM SGP and TWP Sites

AbstractIce cloud column optical depths from the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite and ground‐based Raman lidars (RLs) at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) and Tropical Western Pacific (TWP) sites are compared for 8 and 4 years, respectively. The cloudy mean ice cloud column optical depths from CALIPSO Version 4 data product with a horizontal resolution of 5 km are 78% (63%) larger than those from the RLs with a resolution of 10 min/30 m at SGP (TWP) for collocated transparent profiles. The main difference at SGP is caused by the lidar ratio that for CALIPSO is related to its treatment of the multiple scattering factor. The main difference at TWP is caused by the averaging resolution. Large differences in the optical depth distribution between the CALIPSO and RL are found for small optical depths at both sites, which are due to optically very thin clouds only detectable by the RLs. The differences in ice cloud column optical depth distributions and their mean values between the CALIPSO and RL can largely be reconciled over both sites after accounting for the averaging resolutions, lidar ratios along with the CALIPSO multiple scattering factor treatment, sensitivity to optically very thin ice clouds, and definition of transparent profiles. This work also examines in detail the lidar ratios that are directly observed by the RLs, which does not support the temperature‐dependent parameterizations of ice cloud lidar ratio and multiple scattering factor used in CALIPSO's Version 4 data product.

Using AIRS and ARM SGP Clear‐Sky Observations to Evaluate Meteorological Reanalyses: A Hyperspectral Radiance Closure Approach

AbstractUsing the ground‐based measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and spectral radiance from the Atmospheric Infrared Sounder (AIRS) on National Aeronautics and Space Administration Aqua, we evaluate the temperature and humidity profiles from European Center for Medium Range Weather Forecasting ERA‐Interim and Modern‐Era Retrospective analysis for Research and Applications Version 2 reanalyses. Four sets of synthetic AIRS spectra are calculated using 51 clear‐sky sounding profiles from the ARM SGP observations, the collocated AIRS L2 retrievals and the two reanalyses, respectively. A subset of AIRS channels sensitive to temperature, CO2, or H2O but not to other trace gases is chosen and further categorized into different groups according to the peak altitudes of their weighting functions. Synthetic radiances are then compared to the observed AIRS radiances for each group. For all groups, the observed AIRS radiances agree well with the synthetic ones based on the ARM SGP soundings or the AIRS L2 retrievals. The brightness temperature (BT) differences are within ±0.5 K. For two reanalyses, BT differences in all temperature‐sensitive groups are generally within ±0.5 K; but the mean BT differences in all groups sensitive to both T and H2O are negative. Together, they suggest a wet bias in the free troposphere in both reanalyses. Moreover, such BT differences can be seen in the analysis of AIRS clear‐sky radiances over the entire 30–40°N zone. A grid‐search retrieval suggests that 9–30% reduction for reanalysis humidity between 200 and 800 hPa is needed to correct such wet bias.

Comparison of Daytime Low‐Level Cloud Properties Derived From GOES and ARM SGP Measurements

AbstractLarge‐scale satellite data are critical for both verifying and improving general circulation model parameterizations of clouds and radiation for climate prediction. For reliable application of satellite data sets in cloud processes and climate models, it is important to have a reasonable estimate of the errors in the derived cloud properties. The daytime single‐layered low‐level cloud properties retrieved by the Geostationary Operational Environmental Satellite system (GOES) are compared with ground‐based observations and retrievals over the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility from June 1998 to December 2006. The GOES retrievals are made via the Visible‐Infrared Solar‐infrared Split‐window Technique. They are spatially averaged within a 0.15° × 0.15° box centered on the ARM SGP site, and the ARM surface observations are temporally averaged ±15 min around the GOES scans to produce collocated pairs. Comparisons are made for monthly means, diurnal means, and one‐to‐one GOES and ARM collocated pairs. GOES Teff is highly correlated with ARM Ttop cloud temperature, having an R2 value of 0.75, though GOES exhibits a cold bias. GOES‐retrieved τ and liquid water path have very good agreement with ARM retrievals with R2s of 0.45 and 0.47, while re (GOES), on average, is about 2 μm greater than ARM re. An examination of solar and viewing geometry has shown that GOES‐retrieved mean re and τ values are impacted by solar zenith angle and especially scattering angle, which is not unexpected and needs to be accounted for by users.

Heterogeneity in warm-season land-atmosphere coupling over the U.S. Southern Great Plains.

Heterogeneity in warm-season (May-August) land-atmosphere (LA) coupling is quantified with the long-time, multiple-station measurements from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program and the moderate-resolution imaging spectroradiometer (MODIS) satellite remote sensing at the Southern Great Plains (SGP). We examine the coupling strength at 7 additional locations with the same surface type (i.e., pasture/grassland) as the ARM SGP central facility (CF). To simultaneously consider multiple factors and consistently quantify their relative contributions, we apply a multiple linear regression method to correlate the surface evaporative fraction (EF) with near-surface soil moisture (SM) and leaf area index (LAI). The observations show moderate to weak terrestrial segment LA coupling with large heterogeneity across the ARM SGP domain in warm-season. Large spatial variabilities in the contributions from SM and LAI to the EF changes are also found. The coupling heterogeneities appear to be associated with differences in land use, anthropogenic activities, rooting depth, and soil type at different stations. Therefore, the complex LA interactions at the SGP cannot be well represented by those at the CF/E13 based on the metrics applied here. Overall, the LAI exerts more influence on the EF than does the SM due to its overwhelming impacts on the latent heat flux. This study complements previous studies based on measurements only from the CF and has important implications for modeling LA coupling in weather and climate models. The multiple linear regression provides a more comprehensive measure of the integrated impacts on LA coupling from several different factors.

Aerosol properties and their impacts on surface CCN at the ARM Southern Great Plains site during the 2011 Midlatitude Continental Convective Clouds Experiment

Aerosol particles are of particular importance because of their impacts on cloud development and precipitation processes over land and ocean. Aerosol properties as well as meteorological observations from the Department of Energy Atmospheric Radiation Measurement (ARM) platform situated in the Southern Great Plains (SGP) are utilized in this study to illustrate the dependence of continental cloud condensation nuclei (CCN) number concentration (NCCN) on aerosol type and transport pathways. ARM-SGP observations from the 2011 Midlatitude Continental Convective Clouds Experiment field campaign are presented in this study and compared with our previous work during the 2009–10 Clouds, Aerosol, and Precipitation in the Marine Boundary Layer field campaign over the current ARM Eastern North Atlantic site. Northerly winds over the SGP reflect clean, continental conditions with aerosol scattering coefficient (σsp) values less than 20 Mm−1 and NCCN values less than 100 cm−3. However, southerly winds over the SGP are responsible for the observed moderate to high correlation (R) among aerosol loading (σsp < 60 Mm−1) and NCCN, carbonaceous chemical species (biomass burning smoke), and precipitable water vapor. This suggests a common transport mechanism for smoke aerosols and moisture via the Gulf of Mexico, indicating a strong dependence on air mass type. NASA MERRA-2 reanalysis aerosol and chemical data are moderately to highly correlated with surface ARM-SGP data, suggesting that this facility can represent surface aerosol conditions in the SGP, especially during strong aerosol loading events that transport via the Gulf of Mexico. Future long-term investigations will help to understand the seasonal influences of air masses on aerosol, CCN, and cloud properties over land in comparison to over ocean.

The Diurnal Cycle of Clouds and Precipitation at the ARM SGP Site: An Atmospheric State‐Based Analysis and Error Decomposition of a Multiscale Modeling Framework Simulation

AbstractLong‐term reflectivity data collected by a millimeter cloud radar at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to examine the diurnal cycle of clouds and precipitation and are compared with the diurnal cycle simulated by a Multiscale Modeling Framework (MMF) climate model. The study uses a set of atmospheric states that were created specifically for the SGP and for the purpose of investigating under what synoptic conditions models compare well with observations on a statistical basis (rather than using case studies or seasonal or longer time scale averaging). Differences in the annual mean diurnal cycle between observations and the MMF are decomposed into differences due to the relative frequency of states, the daily mean vertical profile of hydrometeor occurrence, and the (normalized) diurnal variation of hydrometeors in each state. Here the hydrometeors are classified as cloud or precipitation based solely on the reflectivity observed by a millimeter radar or generated by a radar simulator. The results show that the MMF does not capture the diurnal variation of low clouds well in any of the states but does a reasonable job capturing the diurnal variations of high clouds and precipitation in some states. In particular, the diurnal variations in states that occur during summer are reasonably captured by the MMF, while the diurnal variations in states that occur during the transition seasons (spring and fall) are not well captured. Overall, the errors in the annual composite are due primarily to errors in the daily mean of hydrometeor occurrence (rather than diurnal variations), but errors in the state frequency (that is, the distribution of weather states in the model) also play a significant role.

The diurnal cycle of clouds and precipitation at the ARM SGP site: Cloud radar observations and simulations from the multiscale modeling framework

AbstractMillimeter Wavelength Cloud Radar (MMCR) data from December 1996 to December 2010, collected at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) site, are used to examine the diurnal cycle of hydrometeor occurrence. These data are categorized into clouds (−40 dBZe ≤ reflectivity &lt; −10 dBZe), drizzle and light precipitation (−10 dBZe ≤ reflectivity &lt; 10 dBZe), and heavy precipitation (reflectivity ≥ 10 dBZe). The same criteria are implemented for the observation‐equivalent reflectivity calculated by feeding outputs from a Multiscale Modeling Framework (MMF) climate model into a radar simulator. The MMF model consists of the National Center for Atmospheric Research Community Atmosphere Model with conventional cloud parameterizations replaced by a cloud‐resolving model. We find that a radar simulator combined with the simple reflectivity categories can be an effective approach for evaluating diurnal variations in model hydrometeor occurrence. It is shown that the MMF only marginally captures observed increases in the occurrence of boundary layer clouds after sunrise in spring and autumn and does not capture diurnal changes in boundary layer clouds during the summer. Above the boundary layer, the MMF captures reasonably well diurnal variations in the vertical structure of clouds and light and heavy precipitation in the summer but not in the spring.

Remote sensing of PM2.5 during cloudy and nighttime periods using ceilometer backscatter

Abstract. Monitoring PM2.5 (particulate matter with aerodynamic diameter d ≤ 2.5 µm) mass concentration has become of more importance recently because of the negative impacts of fine particles on human health. However, monitoring PM2.5 during cloudy and nighttime periods is difficult since nearly all the passive instruments used for aerosol remote sensing are not able to measure aerosol optical depth (AOD) under either cloudy or nighttime conditions. In this study, an empirical model based on the regression between PM2.5 and the near-surface backscatter measured by ceilometers was developed and tested using 6 years of data (2006 to 2011) from the Howard University Beltsville Campus (HUBC) site. The empirical model can explain ∼ 56, ∼ 34 and ∼ 42 % of the variability in the hourly average PM2.5 during daytime clear, daytime cloudy and nighttime periods, respectively. Meteorological conditions and seasons were found to influence the relationship between PM2.5 mass concentration and the surface backscatter. Overall the model can explain ∼ 48 % of the variability in the hourly average PM2.5 at the HUBC site when considering the seasonal variation. The model also was tested using 4 years of data (2012 to 2015) from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, which was geographically and climatologically different from the HUBC site. The results show that the empirical model can explain ∼ 66 and ∼ 82 % of the variability in the daily average PM2.5 at the ARM SGP site and HUBC site, respectively. The findings of this study illustrate the strong need for ceilometer data in air quality monitoring under cloudy and nighttime conditions. Since ceilometers are used broadly over the world, they may provide an important supplemental source of information of aerosols to determine surface PM2.5 concentrations.

Comparison of Satellite-, Model-, and Radiosonde-Derived Convective Available Potential Energy in the Southern Great Plains Region

AbstractConvective available potential energy (CAPE) is one of the physical quantities used by operational meteorologists when issuing severe-weather convective watches and warnings. Recent advances in satellite technology could provide timely observations of atmospheric temperature and water vapor profiles over the continental United States, but only limited validation exists in the literature to characterize uncertainties in CAPE derived from the new satellite sensors. In this study, 10 years of Vaisala, Inc., RS92 radiosonde observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site were matched to overpasses of the NASA Aqua satellite that were made from January 2005 through December 2014. Vertical profiles of temperature and water vapor from the NASA Atmospheric Infrared Sounder (AIRS) were extracted in a region surrounding the DOE ARM SGP central facility near Lamont, Oklahoma. Surface-based CAPE was computed using software consistent with methods used by the National Weather Service Storm Prediction Center. The one-to-one correspondence of the AIRS-derived CAPE with the ARM-radiosonde-derived CAPE has a correlation coefficient of only 0.34. Substitution of the ARM-radiosonde surface values into the AIRS profiles improves the correlation to 0.95. The use of AIRS profiles above the surface level provides surface-based CAPE values that are very similar to those computed from Vaisala radiosondes. These results suggest that a merging of surface observations with satellite-derived thermodynamic profiles could make better use of the satellite spatial coverage and temporal sampling for estimation of CAPE in near–real time.

Evaluation of NASA GISS post-CMIP5 single column model simulated clouds and precipitation using ARM Southern Great Plains observations

The planetary boundary layer turbulence and moist convection parameterizations have been modified recently in the NASA Goddard Institute for Space Studies (GISS) Model E2 atmospheric general circulation model (GCM; post-CMIP5, hereafter P5). In this study, single column model (SCM P5) simulated cloud fractions (CFs), cloud liquid water paths (LWPs) and precipitation were compared with Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) groundbased observations made during the period 2002–08. CMIP5 SCM simulations and GCM outputs over the ARM SGP region were also used in the comparison to identify whether the causes of cloud and precipitation biases resulted from either the physical parameterization or the dynamic scheme. The comparison showed that the CMIP5 SCM has difficulties in simulating the vertical structure and seasonal variation of low-level clouds. The new scheme implemented in the turbulence parameterization led to significantly improved cloud simulations in P5. It was found that the SCM is sensitive to the relaxation time scale. When the relaxation time increased from 3 to 24 h, SCM P5-simulated CFs and LWPs showed a moderate increase (10%–20%) but precipitation increased significantly (56%), which agreed better with observations despite the less accurate atmospheric state. Annual averages among the GCM and SCM simulations were almost the same, but their respective seasonal variations were out of phase. This suggests that the same physical cloud parameterization can generate similar statistical results over a long time period, but different dynamics drive the differences in seasonal variations. This study can potentially provide guidance for the further development of the GISS model.

Can MODIS cloud fraction fully represent the diurnal and seasonal variations at DOE ARM SGP and Manus sites?

AbstractThough cloud fraction (CF) from Moderate Resolution Imaging Spectroradiometer (MODIS) has been widely used, it remains unclear whether it can fully represent the diurnal variations. This study evaluates the time representation (i.e., satellite passes' mean value per day to represent daily average value) error in MODIS CF by using daytime‐only total sky cover and continuous day‐and‐night radar/lidar CF (Active Remote Sensing of Clouds product, ARSCL) from 2000 to 2010 for two Atmospheric Radiation Measurement (ARM) program climate regime sites of Southern Great Plains (SGP) and Manus. By comparing the daily averaged CFs from ARSCL between using all hourly and using the MODIS‐passing‐time observations, it shows a correlation coefficient of 0.93 (0.88) and root mean square deviation (RMSD) of 12.68% (13.27%) over SGP (Manus) site for daily averaged CFs. Differently, it shows a better correlation coefficient of 0.97 (0.97) and smaller RMSD of 2.98% (3.97%) over SGP (Manus) site for monthly averaged CFs. These suggest that considerable errors could be introduced while using the MODIS CF observed at several fixed time points a day to represent average CF at different time scales. Monthly time representation errors have also been evaluated for daytime only and nighttime only, which show even larger values. A further analysis shows that uncertainties caused by the time representation account for about 23% (21%) of the total differences between surface and MODIS CFs over SGP (Manus) site at monthly time scale.

극궤도 위성 관측을 이용한 지표면에서의 태양 복사에너지 도출

본 연구에서는 대기 상단에서 반사된 복사와 지표면에서 흡수된 복사에너지가 서로 선형 관계임을 보인 기존의 알고리즘을 MODerate resolution Imaging Spectroradiometer (MODIS) 관측 자료에 적용시킬 수 있도록 개선하여 하향 및 순 태양 복사에너지를 산출하였다. 비교 검증을 수행하기 위해 Clouds and the Earth's Radiant Energy System (CERES) 센서와 강릉원주대학교(GWNU), 미국의 Atmospheric Radiation Measurement (ARM) 관측소의 지상 일사계 자료를 사용하였다. 지구 에너지 수지와 관련하여 지표면에서의 복사에너지를 비교하기에 앞서 알고리즘을 통한 산출 결과와 CERES 자료의 대기 상단 복사에너지를 비교한 결과, 결정계수가 0.9이상을 보여 상당히 유사함을 보였지만 Root-Mean-Square-Deviation (RMSD) 값이 다소 차이가 있는 것으로 나타났고 하향과 순 태양 복사에너지도 비슷한 결과를 얻었다. 강릉원주대학교 자료와 비교한 결과, 본 연구의 알고리즘을 통해 산출된 결과가 CERES 자료보다 작은 RMSD를 보임으로서 더 높은 정확도를 보였다. 한편, ARM 관측소의 경우 하향 태양 복사에너지의 평균적인 RMSD가 CERES 자료에 비해 다소 크게 산출되었지만 순 태양 복사에너지의 경우 비슷한 결과가 나타났으며 시 공간 해상도를 고려하였을 때 상당히 유사한 경향을 보이고 있음을 확인하였다. 본 연구에서 파악된 문제점에 대한 개선을 통해 향후 지상 관측을 대신하여 위성 관측으로부터 지표면에서의 하향 및 순 태양 복사에너지 자료를 고해상도로 제공하는데 유용하게 활용될 수 있을 것으로 기대된다. In this study, the net solar radiation fluxes at the surface are retrieved by updating an existing algorithm to be applicable for MODerate resolution Imaging Spectroradiometer (MODIS) observations, in which linear relationships between the solar radiation reflected from the top of atmosphere and the net surface solar radiation are employed. The results of this study have been evaluated through intercomparison with existing Clouds and the Earth's Radiant Energy System (CERES) data products and ground-based data from pyranometers at Gangneung-Wonju National University (GWNU) and the Southern Great Plains (SGP) of observatory of Atmospheric Radiation Measurement (ARM) site. Prior to the comparison of the surface radiation energy in relation to the energy balance of the earth, the radiation energy of the upper part of the atmosphere was compared. As a result, the coefficient of determination was over 0.9, showing considerable similarity, but the Root-Mean-Square-Deviation (RMSD) value was somewhat different, and the downward and net solar-radiation energy also showed similar results. The surface solar radiation data measured from pyranometers at Gangneung-Wonju National University (GWNU) and Atmospheric Radiation Measurement (ARM) observatory are used to validate the solar radiation data produced in this study. When compared to the GWNU, The results of this study show smaller RMSD values than CERES data, showing slightly better agreements with the surface data. On the other hand, when compared with the data from ARM SGP observatory, the results of this study bear slightly larger RMSD values than those for CERES. The downward and net solar radiation estimated by the algorithm of this study at a high spatial resolution are expected to be very useful in the near future after refinements on the identified problems, especially for those area without ground measurements of solar radiation.

Improving Parameterization of Entrainment Rate for Shallow Convection with Aircraft Measurements and Large-Eddy Simulation

AbstractThis work examines the relationships of entrainment rate to vertical velocity, buoyancy, and turbulent dissipation rate by applying stepwise principal component regression to observational data from shallow cumulus clouds collected during the Routine Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign over the ARM Southern Great Plains (SGP) site near Lamont, Oklahoma. The cumulus clouds during the RACORO campaign simulated using a large-eddy simulation (LES) model are also examined with the same approach. The analysis shows that a combination of multiple variables can better represent entrainment rate in both the observations and LES than any single-variable fitting. Three commonly used parameterizations are also tested on the individual cloud scale. A new parameterization is thus presented that relates entrainment rate to vertical velocity, buoyancy, and dissipation rate; the effects of treating clouds as ensembles and humid shells surrounding cumulus clouds on the new parameterization are discussed. Physical mechanisms underlying the relationships of entrainment rate to vertical velocity, buoyancy, and dissipation rate are also explored.